Evolutionary lottery of skull and beak morphology
gif provided by the awesome Jen Bright @MorphobeakGeek!
In this exercise we will use a github repo to collaboratively collate and simulate evolutionary trajectories for each participants’ species body size using a simple brownian motion evolutionary model. This assumes evolutionary steps to progress comletely at random. You could say:
it’s a bit of lottery!
Each participant has created and contributed a file specifying the parameters required to simulate and plot their species evolutionary trajectory. We’ve collect all participants’ files in the master repo. Next we need to simulate species trajectories plot them up.
Participants will then get to see the skull and beak shape corresponding to their species relative body size!
setup
First we load the required packages and create some objects to compile data on trait evolution for each species.
library(dplyr)
library(ggplot2) #3.5.1
library(plotly) #4.10.4
set.seed(1)
t <- 0:100 # generate time vector
dt <- NULL # generate object to compile time-series data
cols <- NULL # generate object to compile trendline coloursSimulate trait evolution, iterate over all species files in
params/ folder
We’ll use the parameters supplied in your scripts to generate brownian trait evolution trendline for each species.
#getting the file names for everything except the template that has undefined values
spp.files <- dir("params/")[dir("params/") != "params_tmpl.R"]
for(spp in spp.files){
# source parameters for each species
source(file.path("params", spp))
# generate trait evolution time-series and compile plotting data
dt <- rbind(dt, data.frame(t,
trait = c(0, rnorm(n = length(t) - 1, sd = sqrt(sig2)) |> cumsum()),
species = species.name))
cols <- c(cols, color)
}Plot trait evolution timeseries
Use the data generated to plot all species.
# Specify the order of species based on the order of colors in cols to stop a mismatch in colours
dt$species <- factor(dt$species, levels = unique(dt$species))
# Create the ggplot object
p <- ggplot(data = dt, aes(x = t, y = trait, group = species, colour = species)) +
geom_line() +
scale_colour_manual(values = cols)
# Plot the results
ggplotly(p)Skulls! find the skull associated with your species:
Skulls are organised from largest to smallest. The largest skulls are vulture-like, (e.g. no. 50, the Andean condor Vultur gryphus) and the smallest are falconet-like, (e.g. no. 1 Collared falconet Microhierax caerulescens)
No: 31 anas_krystallinus
No: 30 lux_fermonta
No: 28 jus_saurius
No: 26 yozw_fermonta
No: 26 lupus_castri
No: 22 jose_luxmont
No: 21 malika_rocks
No: 21 lux_fermonta
No: 2 Junste
Session Info
sessioninfo::session_info()## Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", :
## running command '"quarto"
## TMPDIR=C:/Users/zerin/AppData/Local/Temp/RtmpkXGSxX/filea4d871f9709b -V' had
## status 1## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
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## os Windows 11 x64 (build 26100)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate Chinese (Simplified)_China.utf8
## ctype Chinese (Simplified)_China.utf8
## tz Europe/Berlin
## date 2025-06-14
## pandoc 3.4 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
## quarto NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe
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